The advent of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has opened new horizons in industrial engineering and operations management. This paper explores the integration of Generative AI and Retrieval-Augmented Generation (RAG) within industrial settings, focusing on both practical applications and theoretical advancements. We examine how these technologies can optimize operations, improve decision-making, and address complex industrial challenges.

On the application front, we discuss case studies where Generative AI and RAG have been deployed to enhance supply chain management, predictive maintenance, and process optimization. For instance, LLMs can analyze vast amounts of operational data to generate insights on production bottlenecks, while RAG can augment these models by incorporating real-time data retrieval, ensuring up-to-date and contextually relevant outputs.

From a theoretical perspective, we delve into the challenges of adapting LLMs to industrial contexts, such as handling domain-specific jargon, ensuring data privacy, and maintaining model robustness. We also explore advancements in model architectures and training methodologies that enhance the applicability of Generative AI in operations management.

Finally, we address the ethical and practical considerations of deploying these technologies, including data security and workforce implications. By bridging the gap between theoretical research and industrial applications, this paper aims to provide a comprehensive overview of the potential of Generative AI and RAG in revolutionizing industrial engineering and operations management.

SWAPNIL MORANDE

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Enhancing Industrial Operations Management through Generative AI and Retrieval-Augmented Generation: Applications and Theoretical Insights